Saturday, 1 October 2011

The Truth About Fieldwork Data Collection

Skidding in late like one of my students handing in homework, here's my submission for the Accretionary Wedge #38, with the hope that since Anne's deadline was "before you go to bed" and that there may be some bloggers in Alaska who are still up, and that crucially Anne may still be asleep for another hour or so, I can sneak in my post.

The theme is "Back to school", and I've been back at school for three weeks (many more if you include enrolment and induction). I am teaching my favourite A-level topic - "The Natural Environment and Species Survival" (and crucially have succeeded in palming off all the immunology and infection stuff on my colleague), involving a lot of evolution, climatology and ecological principles. The students covered a bit of biodiversity and conservation last year but this builds on it.

The A2 class of 2011 on fieldwork - all now off to university, and since this photo now appears in the prospectus I feel okay about posting it online...

For me, one of the most important things I can teach my students is how to do fieldwork. I appreciate that few of my students will ever go into a field-based science (although I am delighted to report that the student I dispatched to do Palaeobiology & Evolution at Portsmouth is having a fantastic time), but it is a superb skill. For the past two years the A2s haven't had a choice - their coursework component has been field-based. This year I may be giving the students an option to do lab-based work instead, but I will still strongly encourage them to still choose a fieldwork project.

It is difficult for the students to think of an original project. Lab-based projects, if unoriginal, give predictable results, and students are perhaps simply repeating the same experiment over and over again through the years. I know I looked at Elodea, photosynthesis and light intensity for my A-level practical - yawn. What I like about fieldwork is its unpredictability. Over the short period of time we have in the field, students essentially choose an area and study biotic and abiotic factors - proximity to hedges or streams, pH of soil, amount of sunlight received, soil moisture, number of species. Despite using a small private nature reserve, and having done this two years running, I have yet to see two projects with the same results - they always choose a different transect, or have a different idea about how frequently to take measurements.

And that is the the crux of it all. I had a student before the summer who, on a short practical quadrat-chucking exercise on campus complained, "This is crap, there's hardly anything here and it makes no sense". Essentially I said to him:

"But don't you see? There is hardly anything here. And you're measuring exactly what is there - you have no expectations of what the answer should be, so all you are doing is pure data collection. Collecting data without an idea of what the results should be is as close as you'll get to eliminating your own bias in ecological fieldwork. Also, do you realise that you are the first person to ever collect this data? No one has ever measured that species, on that transect before."

I saw the realisation dawn on him - this is what science is actually about. Doing an experiment and collecting data without having any idea what the result will be. I dislike the hypothesis-driven way of doing science. It can end up putting the cart before the horse. I'd rather approach a scientific investigation with "I wonder what happens if I do X" than "I think if I do X then Y will happen". There are, of course, times when either is appropriate, but when students are taking their first tentative steps towards doing "real" science, with no teachers' notes and "right answers", the prospect of Y not happening can be disheartening.

So I would encourage my colleagues to be more honest about data collection in science, and to give the students the chance to investigate questions you don't know the answer to (admittedly, easier once dealing with undergrads). Real science rarely gives you a nice straight line graph or results you can use to prove or disprove your hypothesis. Real science is anomalous, full of errors (all ready to be minimised by the prospective scientists as they hone their technique) and deliciously exciting.

1 comment:

Don't worry, I think you have safely snuck n your submission under the wire ;-)

I remember Elodea in biology A-level! And, if I recall correctly, it was a perfect example of what you're talking about: it was virtually impossible to get anything that remotely looked like an inverse square curve that you were 'supposed' to get - at least not without some creative massaging of the data (such as subjective omission of aberrant data points). Not quite the right lesson to be passing on, I feel...